{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Introduction to Radar Using Python and MATLAB***\n",
    "## Andy Harrison - Copyright (C) 2019 Artech House\n",
    "<br/>\n",
    "\n",
    "# Cloud/Fog Attenuation\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Referring to Section 2.9.4, the attenuation of electromagnetic waves due to clouds and fog is of particular importance not only to airborne and ground-based upward-looking radars, but also to vehicle based radars dealing with automatic emergency braking, radar systems for mining, agriculture, and search and rescue.  Many of these sensors operate in higher frequency regimes where the attenuation due to fog and cloud becomes significant. Considering clouds and fog made up of small droplets less than $0.01$ cm, the Rayleigh approximation allows the attenuation within the cloud or fog to be written as (Equation 2.163)\n",
    "\n",
    "$$\n",
    "\\gamma = k_l(f, T) \\, M \\hspace{0.5in} \\text{(dB/km)}\n",
    "$$\n",
    "\n",
    "\n",
    "This expression is valid for frequencies up to about $200$ GHz.  Typical values for the liquid water density in fog is $0.05$ g/m^{3} for medium fog with a visibility of about $300$ meters, and $0.5$ g/m^{3} for heavy fog with a visibility of about $50$ meters. \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by getting library path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lib_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the start and end frequencies (GHz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "frequency_start = 1\n",
    "\n",
    "frequency_end = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the frequency array using the `linspace` routine from `scipy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import linspace\n",
    "\n",
    "frequency = linspace(frequency_start, frequency_end, 2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the liquid water temperature (K) and the liquid water density (g/m^3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "liquid_water_temperature = 290\n",
    "\n",
    "liquid_water_density = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the keyword args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "kwargs = {'frequency': frequency,\n",
    "\n",
    "          'liquid_water_temperature': liquid_water_temperature,\n",
    "\n",
    "          'liquid_water_density': liquid_water_density}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the attenuation using the `attenuation` routine from `cloud_fog`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Libs.wave_propagation import cloud_fog\n",
    "\n",
    "gamma = cloud_fog.attenuation(**kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the cloud/fog attenuation using the routines from `matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "# Set the figure size\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
    "\n",
    "\n",
    "\n",
    "# Display the results\n",
    "\n",
    "plt.loglog(frequency, gamma)\n",
    "    \n",
    "    \n",
    "\n",
    "# Set the plot title and labels\n",
    "\n",
    "plt.title('Cloud or Fog Attenuation', size=14)\n",
    "\n",
    "plt.xlabel('Frequency (GHz)', size=12)\n",
    "\n",
    "plt.ylabel('Specific Attenuation (dB/km)', size=12)\n",
    "\n",
    "\n",
    "\n",
    "# Set the tick label size\n",
    "\n",
    "plt.tick_params(labelsize=12)\n",
    "\n",
    "\n",
    "\n",
    "# Turn on the grid\n",
    "\n",
    "plt.grid(linestyle=':', linewidth=0.5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
